9 papers
Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation
Bingjia Huang, Xin Ding, Fu Chen +5
Egocentric video offers a scalable source of physical interaction experience, yet translating it into robot-executable knowledge and enabling continual adaptation remain challengin…
Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
Fu Chen, Xin Ding, Bingjia Huang +8
Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argue that robots need to learn…
Zetta : An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence
Xin Ding, Liang Mi, Mingzhe Huang +12
Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing…
MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents
Xin Ding, Xinrui Wang, Yifan Yang +9
Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monolithic Memory Injection (AMMI…
OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
Xiangyu Li, Huaizhi Tang, Xin Ding +3
Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct t…
EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities
Travis Davies, Yiqi Huang, Alexi Gladstone +5
Implicit policies parameterized by generative models, such as Diffusion Policy, have become the standard for policy learning and Vision-Language-Action (VLA) models in robotics. Ho…
Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer
Travis Davies, Yiqi Huang, Yunxin Liu +3
Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains chall…
STRIDE: Automating Reward Design, Deep Reinforcement Learning Training and Feedback Optimization in Humanoid Robotics Locomotion
Zhenwei Wu, Jinxiong Lu, Yuxiao Chen +3
Humanoid robotics presents significant challenges in artificial intelligence, requiring precise coordination and control of high-degree-of-freedom systems. Designing effective rewa…
Generalized Robot Learning Framework
Jiahuan Yan, Zhouyang Hong, Yu Zhao +4
Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, i…